Artificial intelligence augumented document capture and processing systems and methods

ABSTRACT

A document capture server receives a document image from a document capture client and processes the image into an electronic document containing textual content. During capture, the document capture server determines a graphical layout of the document, extracts keywords from the document, classifies the document accordingly, and calls an artificial intelligence (AI) platform to gain insights on the textual content. The AI platform analyzes the textual content and returns additional, insightful data such as a sentiment of the textual content. The document capture server can validate the additional data, integrate the additional data in a process or workflow, and/or provide the textual content and the additional data to a content repository or a computing facility operating in an enterprise computing environment. The document capture server can provide validated data to the AI platform to improve future analyses by the AI platform.

TECHNICAL FIELD

This disclosure relates generally to the field of document capture andprocessing. More particularly, this disclosure relates to systems,methods, and computer program products for artificial intelligenceaugmented document capture and processing.

BACKGROUND OF THE RELATED ART

Document capture is a field of computer technology that provides acomputer system with the abilities to capture documents in a variety ofways, for instance, scanning paper documents and storing them as digitalimages, importing electronic documents in one format and converting themto another format, etc. This kind of document processing focuses on theformats of the captured documents and is agnostic to the contents of thecaptured documents. Accordingly, in an enterprise computing environment,document capture is often combined with a content management system sothat the captured documents can be processed and managed appropriatelywith respect to their contents.

SUMMARY OF THE DISCLOSURE

An object of the invention is to change the division of labor inprocessing captured documents by leveraging artificial intelligence (AI)to enhance document capture and to provide insight on unstructured databefore providing the captured documents to a downstream computingfacility such as a content management system operating in an enterprisecomputing environment.

Generally, this object can be achieved in a document capture servercomputer that can import/capture/ingest documents, convert them toappropriate format(s), enhance the documents, and apply opticalcharacter recognition (OCR) to the documents. Additionally, the documentcapture server computer can determine their graphical layoutsrespectively, extract keywords contained in the documents, and classifythe documents based on their respective graphical layouts and/orextracted keywords. The document capture server computer can alsoperform zonal extraction for certain documents such as forms and performfreeform extraction for regular expression matching. However, beforeproviding the documents to a downstream computing facility such as acontent management system (CMS) or an enterprise content management(ECM) repository, the document capture server computer is operable togain insights on the contents of the documents.

In some embodiments, during capture, the document capture servercomputer calls an AI platform with text and information that thedocument capture server computer has on the documents and/or hasdetermined from the document capture server computer. The AI platformhas an advanced natural language processing (NLP) text mining engine.The call from the document capture server computer specifies NLP textmining function(s) (e.g., sentiment analysis, concept extraction,category, etc.) to perform on the text and, in response, the AI platformreturns insightful data such as a sentiment, concept, category, etc. Thedocument capture server computer can validate (e.g., through integritychecks, user validation, etc.) the additional data from the AI platform.The documents and the additional data provided by the AI platform canthen be integrated with processes run by the document capture serverand/or other enterprise-grade systems (e.g., CMS, ECM, recordsmanagement, etc.).

On the AI platform side, the invention disclosed herein can also providebenefits. For example, classification and/or informationextracted/determined from a document by the document capture server andincluded in a request from the document capture server computer can beused by the AI platform in supervised machine learning of new graphicallayouts and/or NLP concepts and entities.

In some embodiments, an AI-augmented document capture and processingmethod can include a document capture server computer receiving, from adocument capture module running on a client device, an image of a paperdocument and processing the image into an electronic document. Theprocessing can be performed at least in part on the document captureserver computer or by the document capture module on the client device.Examples of processing can include, but are not limited to, a formatconversion on the image, an image enhancement on the image, or anoptical character recognition procedure on the image.

The document capture server computer can determine a graphical layout ofthe electronic document and/or extract keywords from the textual contentof the electronic document. Based on the graphical layout of theelectronic document and/or the keywords extracted from the electronicdocument, the document capture server computer can classify theelectronic document. At this processing stage, the document captureserver computer can also perform a zonal extraction of a form in theimage and/or a freeform extraction for a regular expression in theimage. The graphical layout of a document may indicate that the documentcontains structured data (e.g., a credit card application, a parts orderform, etc.). If so, the document capture server computer can proceed toextract the structured data. For unstructured data, the document captureserver computer can extract certain keywords from the textual content ofthe unstructured data, determine a class based on the extractedkeywords, and obtain additional data (insights) from an AI platform.

In some embodiments, the document capture server computer can make acall (e.g., a web service call) to an AI platform (e.g., through anapplication programming interface (API) of an NLP text mining engineoperating on a server computer of the AI platform). The call can containthe textual content, or a portion thereof, of the electronic documentand the classification information of the electronic document, asdetermined by the document capture server computer. In some cases, theweb service call can also include an identification of a knowledge baseaccessible by the AI platform server computer, the knowledge basespecific to a class or type of the electronic document. The AI platformcan (e.g., through the NLP text mining engine) analyze the textualcontent, or the portion thereof, of the electronic document and returnadditional data such as a sentiment or tonality of the textual content,or the portion thereof, of the electronic document. Examples of theadditional data can include, but are not limited to, at least an entity,a summary, a category, or a concept that the AI platform learned fromthe textual content, or the portion thereof, of the electronic document.

The document capture server computer can validate the additional datareturned by the AI platform, integrate the electronic document and theadditional data with another system or process, and/or provide theelectronic document and the additional data to a content repository or acomputing facility operating in an enterprise computing environment. Insome cases, a validated concept or entity from the data validationprocess can be provided to the AI platform. In turn, the AI platform canutilize the validated concept or entity from the data validation processperformed by the document capture server computer in applying supervisedmachine learning to improve future analyses by the AI platform.

One embodiment comprises a system comprising a processor and anon-transitory computer-readable storage medium that stores computerinstructions translatable by the processor to perform a methodsubstantially as described herein. Another embodiment comprises acomputer program product having a non-transitory computer-readablestorage medium that stores computer instructions translatable by aprocessor to perform a method substantially as described herein.Numerous other embodiments are also possible.

These, and other, aspects of the disclosure will be better appreciatedand understood when considered in conjunction with the followingdescription and the accompanying drawings. It should be understood,however, that the following description, while indicating variousembodiments of the disclosure and numerous specific details thereof, isgiven by way of illustration and not of limitation. Many substitutions,modifications, additions, and/or rearrangements may be made within thescope of the disclosure without departing from the spirit thereof, andthe disclosure includes all such substitutions, modifications,additions, and/or rearrangements.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings accompanying and forming part of this specification areincluded to depict certain aspects of the invention. A clearerimpression of the invention, and of the components and operation ofsystems provided with the invention, will become more readily apparentby referring to the exemplary, and therefore non-limiting, embodimentsillustrated in the drawings, wherein identical reference numeralsdesignate the same components. Note that the features illustrated in thedrawings are not necessarily drawn to scale.

FIG. 1 depicts a flow chart illustrating an example of an overallprocess for AI-augmented document capture and processing according tosome embodiments.

FIG. 2 depicts a diagrammatic representation of an example of an overallarchitecture including a document capture client, a document captureserver, and an AI platform according to some embodiments.

FIG. 3A depicts an example of an original document.

FIG. 3B depicts a version of the document shown in FIG. 3A, withinformation that can be captured by a document capture server computeraccording to some embodiments.

FIG. 3C depicts a version of the document shown in FIG. 3A, withadditional data that can be captured by an NLP text mining engine of anAI platform according to some embodiments.

FIG. 4 depicts a diagrammatic representation of an example of a paperdocument being captured on a smart phone through a document captureclient or mobile application according to some embodiments.

FIG. 5 depicts an example of additional data mined from the documentshown in FIG. 4 and returned to the document capture client or mobileapplication according to some embodiments.

FIG. 6 depicts a flow chart illustrating an example of a method forAI-augmented document capture and processing according to someembodiments.

FIG. 7 depicts a diagrammatic representation of a distributed networkcomputing environment where embodiments disclosed can be implemented.

DETAILED DESCRIPTION

The invention and the various features and advantageous details thereofare explained more fully with reference to the non-limiting embodimentsthat are illustrated in the accompanying drawings and detailed in thefollowing description. Descriptions of well-known starting materials,processing techniques, components, and equipment are omitted so as notto unnecessarily obscure the invention in detail. It should beunderstood, however, that the detailed description and the specificexamples, while indicating some embodiments of the invention, are givenby way of illustration only and not by way of limitation. Varioussubstitutions, modifications, additions, and/or rearrangements withinthe spirit and/or scope of the underlying inventive concept will becomeapparent to those skilled in the art from this disclosure.

Today, a document capture and extraction tool can enable a user to scana paper document into an image (e.g., a .TIFF file, a .JPG file, etc.)and store the image file electronically (e.g., in a computer memory, anon-transitory computer-readable medium, etc.). In some cases, thedocument capture and extraction tool can include an OCR and/or otherrecognition functions (e.g., a barcode reader) and the user can choosewhether and which recognition function should be applied to the scannedimage.

At the consumer side, such a document capture and extraction tool can beimplemented on a scanner device, copier, or a printer. At the enterpriseside, a document capture solution is significantly more complex and, dueto the volume of documents that must be processed at any given time on acontinuing basis, is usually automated and implemented on a servermachine or a cluster of server machines operating in an enterprisecomputing environment.

Generally, an enterprise document capture solution encompasses powerfulsoftware tools for handling high-volume scanning, classifying, andaggregating information from document collections. Information such asmetadata and textual information thus obtained from the documents can beprovided to enterprise-grade backend systems such as a documentmanagement system (DMS), an ECM system, etc. This allows theenterprise-grade backend systems to store and manage valuableinformation originated from paper documents.

Traditionally, the processing done by an enterprise document capturesolution focuses on transforming paper documents to electronic ones.While OCR may be applied to a document image to extract textualinformation that can then be used to classify the content contained inthe document image, no additional analyses are conducted by theenterprise document capture solution to gain insight on the content.

Embodiments disclosed herein improve traditional enterprise documentcapture solutions with AI-augmented advance analytics. FIG. 1 depicts aflow chart illustrating an example of an overall capture process 100 forAI-augmented document capture and processing according to someembodiments.

In some embodiments, capture process 100 can include capturing contentcontained in a paper document (101). This step can take place on theclient side and/or on the server side. FIG. 2 depicts a diagrammaticrepresentation of an example of overall architecture 200 including adocument capture module on a client device on the client side and adocument capture server computer and an AI platform on the server side,according to some embodiments. The document capture server computer canprovide services (e.g., a document capture service, an AI-augmenteddocument capture service, an AI-augmented text mining/capture service,etc.) over a network to user devices and can operate in many suitablecomputing environments, for instance, on the premises of an enterprise,in a cloud computing environment, etc. OpenText™ Cloud, available fromOpen Text, headquartered in Waterloo, Canada, is an example of asuitable cloud computing environment.

As an example, on the client side, a user may start a document capturemodule, service, or application (e.g., document capture module 210) onthe user's device (e.g., client device 201) and scan or take a pictureof the paper document (e.g., through user interface 212). The documentcapture module can then send the picture or image of the paper documentto a document capture server computer. As discussed below, the documentcapture module can include an image enhancement function. The picture orimage may be automatically enhanced, or the user may use the imageenhancement function to enhance the picture or image on the user device.Example user devices can include, but are not limited to, mobiledevices, smart phones, laptop computers, tablet computers, desktopcomputers, portable computing devices, and so on.

On the server side, the document capture server computer (e.g., documentcapture server 220) can import or ingest documents from various sources(e.g., document capture module 210 running on client device 201,scanners, copiers, printers, fax servers, email servers, etc.), convertthem into appropriate file formats (e.g., .PDF, .TIFF, .JPG, .BMP,etc.), enhance the images if/when/where necessary, and perform full textOCR on the images (e.g., as part of document preparation stage 222). Thedocument capture server computer is capable of performing advancedrecognition on structured and/or semi-structure documents (e.g., asdetermined from their graphical layouts). Processing of unstructureddocuments is further described below.

In some cases, the document capture module can perform OCR on the clientside and provide the textual output from the OCR to the document captureserver computer along with the image of the paper document. Informationcaptured this way is unstructured.

The document capture server computer is operable to classify content andextract information such as keywords from the content (105). Thedocument capture server computer can classify a document in multipleways. For instance, the document capture server computer can analyze adocument, determine a graphical layout of the document, and determine aclass or type for the document based on the graphical layout. Likewise,the document capture server computer can analyze a document, identifykeywords in the document, extract those keywords from the document, anddetermine a class for the document based on the keywords extracted fromthe document. At this processing stage (e.g., document classificationand extraction stage 224), the document capture server computer canperform other document analyses such as zonal extraction and freeformextraction for certain types of documents (e.g., documents with forms,documents with regular expressions), etc. Zonal extraction, alsoreferred to as template OCR, can extract text located at a specificlocation or zone within a document.

Unstructured content does not have a fixed, recognizable data structure.The textual information contained therein, however, may have a syntacticor linguistic structure that could offer insights on the semanticmeaning, tonality, concept, entity relationship, etc. conveyed in theunderlying document. While the document capture server computer isextremely proficient in classifying documents (e.g., various types ofdocuments such as invoices, purchase orders, contracts, complaints,forms, insurance claims, etc.), it does not have advanced dataprocessing capabilities like text mining, entity extraction (e.g.,person name, organization name, etc.), concept extraction, sentimentanalysis (e.g., opinion versus fact, positive versus negative, etc.),summarization (e.g., reducing paragraphs down to a few sentences),categorization, etc.

For example, while the document capture server computer can classify adocument as an insurance claim, it cannot determine whether it is a homeinsurance claim or an auto insurance claim. However, the AI platform cancomprehend (e.g., through machine learning) that the document isactually a car accident insurance claim. As another example, while thedocument capture server computer can extract keywords and phrases, itcannot recognize a person's name and associate that person's name withan account number. However, the AI platform can learn how a named entity(e.g., a person's name) is associated with another named entity (e.g.,an account) and recognize the relationship between such entities.

In some embodiments, the document capture server computer is operable togain such insights through an AI platform (e.g., AI platform 230) (110).As explained further below, this may entail calling an NLP text miningengine (e.g., NLP text mining engine 235) of the AI platform (e.g., viaa web service call to an API of NLP text mining engine 235) with thetextual information obtained from the unstructured content and anyinformation (e.g., a content class) determined from the unstructuredcontent by the document capture server computer.

In some cases, the web service call can optionally include anidentification of a knowledge base (e.g., a knowledge base in data store240) accessible by the AI platform server computer. The knowledge basecan be specific to the content class. The AI platform can (e.g., throughNLP text mining engine 235) analyze the textual information and returnmined results, such as a sentiment or tonality of the textualinformation, a named entity, a summary, a category, a concept, etc.,that the AI platform has learned from the textual information and/or thecontent class provided by the document capture server computer.

Before describing capture process 100 further, a discussion of NLP textmining engine 235 might be helpful. NLP text mining engine 235 isconfigured for performing a plurality of text mining functions,including entity extraction, sentiment analysis, summarization,categorization, concept extraction, etc.

For entity extraction, NLP text mining engine 235 is operable to extractnamed entities. For instance, suppose a document describes that aspecific company is releasing a new product. Based on linguistic rulesand statistical patterns, NLP text mining engine 235 can extract thecompany's name, the new product name, etc. from the document. Alloccurrences of an entity type may be extracted. For synonyms, acronyms,and variations thereof, an authority file may be used. An authority filerefers to a controlled vocabulary of terms and cross-reference termsthat assists entity extraction to return additional relevant items andrelated metadata (e.g., geopolitical locations, person names,organization names, trademarks, events, etc.). There can be multipleauthority files, each for a particular controlled vocabulary of termsand cross-reference terms.

Output from entity extraction can be a list of extracted entities withattributes and relevancy ranking. Since text mining is performed at thedocument level, the extracted metadata (e.g., the company's name and thenew product name in this example) can be used to enrich the document.NLP text mining engine 235 can learn how to determine an entity based onprevious examples from which a model has been trained using machinelearning. For example, suppose multiple documents mention a company name“Company X” following a product name “ABC,” NLP text mining engine 235may learn from these examples and determine to add an entity “Company X”for a new document that mentions the product name “ABC”, even if the newdocument does not explicitly mention the company name “Company X.”

For sentiment analysis, NLP text mining engine 235 is operable toprogrammatically examine a piece of content (e.g., a post, a document, atweet, an article, a message, etc.) in an even more fine-grained manner.For instance, for a given sentence in a document that describes acompany releasing a new product, NLP text mining engine 235 is operableto analyze the sentence and determine whether the sentiment for thetotality of the sentence is positive, negative, or neutral. Since NLPtext mining engine 235 also extracts the company name and the productname, the sentiment or tonality detected in a sentence by NLP textmining engine 235 can be associated with an entity or entities (e.g.,the company and/or the product) in the sentence. At the entity level,multiple instances of a given entity can be combined to assess anoverall sentiment value for the entity. In this way, what the documentsays about the product (e.g., a positive tone, a negative tone, or aneutral tone) at various levels (e.g., at the document level, thesentence level, the entity level, etc.) can be captured and leveraged bya content analysis (along with other documents relevant to the companyand the product), for instance, for trend analysis and mood detection.NLP text mining engine 235 can also leverage machine learning to learnhow to determine a sentiment, for instance, by running a machinelearning algorithm that utilizes input data and statistical models(e.g., NLP models or NLP classifiers) to predict an output value (e.g.,a tone value).

For conception extraction, NLP text mining engine 235 is operable toextract key concepts, including complex concepts. For example, conceptscan be identified with an algorithm based on linguistic and statisticalpatterns (e.g., keywords and key phrases). These can include the mostrelevant noun(s) and phrase(s) for a given purpose. The extractedconcepts can be weighted ranked such that they are outputted withrelevancy ranking.

For categorization, NLP text mining engine 235 is operable toprogrammatically examine the input text and determine, according to acontrolled vocabulary (a taxonomy—a scheme of classification), a besttopic for the document and attach the topic to the document. Forinstance, a news article discusses that a president is going to visit acountry. NLP text mining engine 235 is operable to programmaticallyexamine the article, determine that this article concerns foreign affairand/or diplomacy, and add “foreign affair” and/or “diplomacy” asmetadata (e.g., “category=foreign affair” or “topic=diplomacy”) to thearticle, even if the article itself does not literally contain “foreignaffair” or “diplomacy.” Downstream from text mining, these pieces ofmetadata can be used by AI platform 230 in different ways for variousreasons. For instance, the vocabulary of NLP text mining engine 235 canbe enhanced using machine learning techniques. Additionally oralternatively, an immediate change can be made to NLP text mining engine235 or through a user interface. NLP text mining engine 235 is capableof learning how to categorize new content based on previous examplesfrom which a model has been trained using machine learning (e.g., usingtaxonomies, training sets, and rules grouped in a categorizationknowledge base). There can be multiple categorization knowledge bases.Output from categorization can include a list of extracted categorieswith relevancy rankings and a confidence score rating for each category.

Generally, summarization refers to the process of shortening a textdocument in order to create a summary with the major points of theoriginal document. To perform summarization, NLP text mining engine 235is operable to identify the most relevant sentences in a piece ofcontent using, for instance, an output from the categorization, andgenerate a summary with the identified sentences. For instance,sentences with the highest relevancy can be identified, extracted, andincluded in the summary. This is a much more precise way to identifyrelevant content at the sentence level.

Each of the text mining functions of NLP text mining engine 235 can beimplemented as a component of the underlying AI platform 230. Forexample, the sentiment analysis function can be implemented as asentiment analysis component of AI platform 230 and the summarizationfunction can be implemented as a summarization component of AI platform230. OpenText™ Magellan, also available from Open Text, is an example ofa suitable AI platform 230. OpenText™ Magellan is a flexible AI andanalytics platform that combines machine learning, advanced analytics,data discovery, and enterprise-grade business intelligence with theability to acquire, merge, manage, and analyze structured andunstructured big data.

The NLP text mining capabilities of AI platform 230 in some cases can beaccessible through a text mining service (e.g., by making an API call toan API endpoint—a base universal resource locator (URL)—where aninstance of the text mining service is hosted on a server computer of AIplatform 230). The text mining service (which can be a type of webservices) accepts an eXtensible Markup Language (XML) post that containsthe text to be analyzed, as well as what text mining functions to beused. For example, a request to the text mining service of AI platform230 for the tone and sentiment of a text block may contain the followinginformation:

<?xml version=“1.0” encoding=“UTF-8” ?> <AIserver> <text_block> This isa story about Gareth Hutchines. He lives in Farnham in Surrey and worksfor OpenText in Reading. He's pretty cool. </text_block> <Methods><Sentiment/> </Methods> </AIserver>

In response to this request, the text mining service can return thesentiment and tone of each sentence in the text as well as the wholepiece of text or document, as specified in the Methods section (i.e.,the “Sentiment” command) of the request. As a non-limiting example, thetext mining service can return the following:

<?xml version=“1.0” encoding=“UTF-8” standalone=“yes”?> <AIserverVersion=“3.0”> <ErrorID Type=“Status”>0</ErrorID><ErrorDescription>Success</ErrorDescription> <Results> <Sentiment><SentenceLevel> <Sentence> <Text begin=“6” end=“44”>This is a storyabout Gareth Hutchins.</Text> <Subjectivityscore=“10.0075”>fact</Subjectivity> <Tone>neutral</Tone> </Sentence><Sentence> <Text begin=“45” end=“109”>He lives in Farnham in Surrey andworks for Opentext in Reading.</Text> <Subjectivityscore=“9.7272”>fact</Subjectivity> <Tone>neutral</Tone> </Sentence><Sentence> <Text begin=“110” end=“127”>He's pretty cool.</Text><Subjectivity score=“79.8701”>opinion</Subjectivity> <PositiveTonescore=“38.471”/> <NegativeTone score=“24.893”/> <Tone>positive</Tone></Sentence> </SentenceLevel> <DocumentLevel> <Subjectivityscore=“75.0036” distribution=“17.2043”>opinion</Subjectivity><PositiveTone score=“25.3561” distribution=“17.2043”/> <NegativeTonescore=“16.4069” distribution=“0.0”/> <Tone>positive</Tone></DocumentLevel> </Sentiment> </Results>

In some embodiments, the call made by document capture server 220 caninclude a knowledge base identifier. Using different knowledge bases maychange the results returned by AI platform 630. Such a knowledge baseidentifier is not required when making a call to AI platform 630. Insome embodiments, AI platform 630 can determine, as appropriate to thetext contained in the call, which knowledge base (e.g., taxonomy) to usein processing the call.

As a non-limiting example, the International Press TelecommunicationsCouncil (IPTC) is the global standards body of the news media. AIplatform 630 can include an IPTC taxonomy. Below provides a non-limitingexample showing how to utilize the IPTC taxonomy or knowledge base (KB)to summarize a piece of text or text block into one sentence. In thisexample, a command is added to the Methods section as follows:

<Methods> <summarizer> <Sentences>1</Sentences> <KBid>IPTC</KBid></summarizer> </Methods>

Below provides an example of a structure that is returned in the Resultssection:

<Results> <summarizer> <Summary>[A Summary of text]</Summary></summarizer> </Results>

AI platform 230 provides different taxonomies for NLP text mining engine235. Further, a user can create a taxonomy and direct NLP text miningengine 235 to use the user-created taxonomy to perform thesummarization.

Other features of NLP text mining engine 235 can be utilized in asimilar way, for instance, by adding respective methods (e.g., entityextraction, concept extraction, categorization, etc.) to the call. Inresponse to a call, AI platform 230 may utilize its components asdirected by the call to process the text contained in the call andreturn the results accordingly.

Still in capture process 100, the document capture server computer canvalidate the results returned by the AI platform (115). This caninclude, for instance, running data integrity checks on the datareturned by the AI platform and/or prompting an authorized user tovalidate the data returned by the AI platform (e.g., as part of datavalidation stage 226). The latter can ensure what the machine (the AIplatform) understood from reading the unstructured content is consistentor agrees with human understanding of the same content. For example, ifthe AI platform returns a tonality analysis result indicating a negativetone of a letter, the document capture server computer can prompt anauthorized user to validate or invalidate that result. If the machinedid not get the tonality of the letter right (e.g., the tone of theletter is not negative but rather neutral), the document capture servercomputer can provide the user's feedback/correction to the AI platform.The AI platform, in turn, can utilize the user's feedback (to any of theNLP text mining results provided by the AI platform) as part ofsupervised learning (or semi-supervised learning) to improve its futureanalyses.

At the end of capture process 100, the document capture server computercan export or provide the captured content, including the documentimage, the textual information, and the NLP text mining results (“AIdata”) from the AI platform to a content repository or a computingfacility (e.g., ECM 250) operating in an enterprise computingenvironment (120). In some embodiments, the document capture servercomputer can start or cause the start of a business process relating tothe captured content (e.g., business process 260). For instance, aninsurance claim or letter (on paper) is captured and processed by thedocument capture server and, at the end of capture process 100, thedocument capture server can start or trigger a process to process theinsurance claim. As this example illustrates, since the letter hadalready been processed during capture process 100, the document captureserver can provide a rich set of information to jump start the process(e.g., the claimant name, address, the nature of the insurance claim,the type of insurance, or even the sentiment of the letter, etc.). Othertypes of system/process integration are also possible (e.g., contentmanagement, record management, case management, workflow management,etc.).

With capture process 100, documents are processed during capture. Thisallows electronic workflows and/or processes in an enterprise to includepaper documents. Further, a workflow or process can be triggered orotherwise initiated by the capture of a paper document (e.g., submissionof an insurance claim, a loan application, a complaint letter, a requestfor leave, etc.). A non-limiting example of this process will now bedescribed with reference to FIGS. 3A-5.

FIG. 3A depicts an example of original document 300 written by aninsurance policy holder to an automobile insurance company concerning aroad accident that resulted in an insured car being damaged. Document300 represents an example of unstructured content. None of these detailsis initially known to a document capture server computer when document300 was first captured and converted into an electronic format (e.g., animage file).

FIG. 3B depicts a version of document 300 shown in FIG. 3A. Through thedata preparation and classification/extraction stages described above,the document capture server computer is operable to extract pieces ofinformation (e.g., document attributes such as address, policy number,email address, date, and vehicle registration number) that might beuseful for subsequent processing.

FIG. 3C depicts another version of document 300 shown in FIG. 3A. Thisversion shows examples of the kinds of additional data that can becaptured by an NLP text mining engine of an AI platform according tosome embodiments. As described above, the NLP text mining engine candistinguish between entities such as people, places, and organizations,utilizing a dictionary of proper nouns. The AI platform can includeadditional controlled vocabularies (taxonomies) such as cars, musicalinstruments, etc. The NLP text mining engine can continuously learn newvocabularies and normalize data where necessary (e.g., “Sainsbury's” canbe normalized to “Sainsbury”). In the example of FIG. 3C, the NLP textmining engine is able to identify new named entities from document 300.

Although not shown in FIG. 3C, the NLP text mining engine can, asdescribed above, programmatically examine the input text and determinethat the best topic (or category) for document 300 is auto insurance andthat, based on linguistic and statistical patterns (e.g., keywords andkey phrases), the purpose (or concept) of document 300 is to requestinsurance coverage. Further, the NLP text mining engine can determine asentiment for a sentence, paragraph, or entire document 300. As anon-limiting example, the word “damage” in document 300 was followed bythe word “caused,” which is followed by the word “prevent,” which isfollowed by the word “me” in the sentence” “A reasonable amount ofdamage was caused to the door, preventing me from opening it.” Utilizingan NLP model that has been trained using previous auto insurance claims,the NLP text mining engine can determine that this sentence has anegative tone. Each sentence in a paragraph can be analyzed in a similarway. A sentiment or tonality of a paragraph can be determined utilizingindividual tone values from the sentences in the paragraph. Likewise, asentiment or tonality of a document can be determined utilizingindividual values from all the sentences and/or paragraphs in thedocument.

As described above, a paper document may be captured as an image on auser device and sent to a document capture server computer forprocessing. FIG. 4 depicts a diagrammatic representation of anon-limiting example of a paper document (e.g., document 300) beingcaptured on a smart phone through a client document capture module (or amobile application having a document capture function) according to someembodiments. In the example of FIG. 4, once started, the mobileapplication provides a user with three options (which, in this example,are shown as three user interface elements or icons at the bottom of thescreen): a forward function, a document capture function, and an imageenhancement function. The forward function is operable to send acaptured image to an instance of a document capture server running onthe document capture server computer. The document capture functionactivates the camera of the smart phone to take a picture. The imageenhancement function allows the user to adjust the quality of thepicture.

Accordingly, as a non-limiting example, the user can take a picture ofthe paper document, adjust the quality of the picture if necessary ordesired, and send the picture to the document capture server computer.Almost instantaneously (with only operational delay such as networklatency and the mobile device's processing speed), the document captureserver computer can send a response back to the mobile application withAI-augmented processing results.

As illustrated in FIG. 5, the AI-augmented processing results can bedisplayed on the smart phone in the form of a new page by the mobileapplication. In this non-limiting example, the AI-augmented processingresults can include information determined by the document captureserver as well as the additional information that the document captureserver obtained from the NLP text mining engine of the AI platform. Theuser can review the captured information and determine whether todownload, export the captured document, or exit the mobile application.At the backend, the document capture server can take appropriate actionon the additional information that the document capture server obtainedfrom the NLP text mining engine of the AI platform (e.g., validate thedata provided by the NLP text mining engine, export to an ECM system,initiate a backend process, etc.).

In this way, documents can be processed during capture and theprocessing can be augmented utilizing AI. FIG. 6 depicts a flow chartillustrating an example of method 600 for AI-augmented document captureand processing.

In some embodiments, method 600 begins when a document capture server“captures” a document (601). This capture can be facilitated by a useruploading a picture of a paper document (as shown in FIG. 4) or thedocument capture server importing or receiving a document (e.g., animage file) from a data source (e.g., a network scanner, a networkcopier, a network printer, a fax server, an email server, a backendsystem, etc.). The document capture server then performs documentpreparation as described above (which can be done in a pipelineinvolving ingestion, format conversion, image enhancement, full textOCR) (605). This step can produce an electronic document in a formatthat is appropriate for further processing, as well as textualinformation.

The prepared document and the textual information can undergo aclassification process (which can also be done in a pipeline thatincludes graphical classification, keyword classification, zonalextraction, and freeform extraction), to determine the type of thedocument (e.g., a contract, a complaint, a correspondence, a purchaseorder, a credit card application, etc.) based on its content (610). Asdescribed above, at this step, the document capture server can examinethe layout of the document and, from the layout, determine a class ortype of the document. For instance, suppose the layout matches a creditcard application form. Such a document can be considered as containingstructured or semi-structured data and, as such, the document captureserver can extract values from directly from the document and does notneed additional data from an AI platform. In such cases (ofstructured/semi-structured documents), the document capture server canproceed to data validation (e.g., data integrity checks, uservalidation, etc.) without calling the AI platform.

Suppose the document capture server cannot determine a document typefrom the layout (e.g., an email, a letter, etc. that does not have astructure or form), the document capture server can examine the textualcontent of the document, extract keywords from the content, anddetermine a class or type from the keywords thus extracted.

The document capture server can then make a call (e.g., a web servicecall) to an AI platform (e.g., through an API of an NLP text miningengine operating on a server computer of the AI platform) with thetextual information (e.g., the body of the email or letter, theclassification, and the extracted information) (615). Suppose an emailis classified as “accounts payable.” Optionally, the web service callcan include an identification of a knowledge base that is specific to“accounts payable.” The AI platform can have many knowledge basestrained on different types of documents such as account payable, autoinsurance claims, home insurance claims, medical documents, etc. If noknowledge base is specified in the call, the AI platform can determinean appropriate knowledge base to use, for instance, based on a policynumber that the AI platform determines as a car insurance policy number(e.g., based on the policy number format). A benefit of knowing whichknowledge base to use is that it may produce more accurate results aseach knowledge base is trained on a particular type of subject. Forinstance, a medical knowledge base could include information ondifferent kinds of fracture, a home insurance knowledge base couldinclude information on power outage and a 300 lb. salmon spoiled due topower outage, etc.

The AI platform can (e.g., through the NLP text mining engine) analyzethe textual content, or the portion thereof, of the document and returnadditional, insightful data such as a sentiment, a tone, a named entity,a category, a concept, etc., as described above. The document captureserver can receive the additional data from the AI platform in real timeor near real time, with only operational delay (620).

The document capture server computer can validate the additional datareturned by the AI platform, integrate the document and the additionaldata with another process or system, and/or provide the document and theadditional data to a content repository or a computing facilityoperating in an enterprise computing environment (625). As describedabove, in some cases, a validation result (e.g., a validated concept, avalidated named entity, etc.) from the data validation process can beprovided by the document capture server to the AI platform. In turn, theAI platform can utilize the feedback from the document capture server toimprove its future analyses of the same type of documents.

The process described above can be implemented in many ways. Forexample, the document capture server can be configured with a “master”workflow or process that is operable to process a document (e.g., anemail, a letter, a fax, a scan, etc.) and shepherd the document from onestep to another (e.g., ingestion from a data source, documentpreparation, classification/extraction, AI-augmented processing, datavalidation, system/process integration, exportation to an ECM system,initiation of a business process, feedback to the AI platform, etc.).This master document capture workflow or process flow can be defined bya user of the document capture server (e.g., through a workflow facilityor user interface with a workflow function). Throughout the masterdocument capture workflow or process flow, the document can beidentified via a globally unique identifier assigned by the documentcapture server computer (e.g., upon capture of the document).

Such a master document capture workflow or process flow may includeworkflow tasks such as determining a document type (e.g., from varioustypes of documents such as invoices, purchase orders, contracts,complaints, forms, insurance claims, etc.), parsing text from thedocument, calling the AI platform with the parsed text (e.g., a block oftext) and parameters specifying AI service(s) (e.g., a parameter fornamed entities such as people or organization names, or everything, inthe block of text), receiving results from the AI platform (e.g., peoplenames such as “Gareth,” organization names such as “Open Text,” etc.),determining whether the results are useful (e.g., whether “Gareth” and“Open Text” are relevant to the document type), and taking anappropriate action or causing an appropriate action to be taken (e.g.,responsive to “Open Text” being relevant to the document type, adding“Open Text” as an organization for the document type or, responsive to“Open Text” not being relevant to the document type, removing “OpenText” as an organization from documents of the same type in the future,responsive to an indication of a car accident insurance claim,initiating a car accident insurance claim workflow, responsive to anindication of a customer complaint about a product, forwarding thecustomer complaint to a supervisor or customer service representative,responsive to a request for refund of overpayment, issuing a refund,etc.). Such actions can include those performed by the document captureserver computer, a subsequent computing facility such as an ECM system,or a user.

Additional tasks can be triggered. For instance, suppose “Open Text” isadded as an organization, the master document capture workflow orprocess flow may further include making another web service call to theAI platform, indicating that “Open Text” is added as an organization.This feedback can be used by the AI platform to enhance its entityextraction, concept extraction, sentiment analysis, etc. As anotherexample, the master document capture workflow or process flow mayfurther include adding a weight (i.e., putting a bias) to a namedentity. For instance, a named entity “London” can weigh more whenmentioned in close proximity to “UK,” and can weigh less when mentionedin close proximity to “Canada.” Such weights can be passed to the AIplatform for use in creating concepts (e.g., this email is about“Gareth,” “Open Text,” and “coffee” and, as such, the AI platform canupdate a knowledge base to include “coffee” as a concept associated witha document containing “Gareth” and “Open Text”). All of the tasks in themaster document capture workflow can be accomplished in real time (or innear real time with only operational delay), in one process flow, andseamlessly integrating document capture, AI analytics, and documentmanagement to achieve an intelligent, AI-driven efficient documentcapture solution.

FIG. 7 depicts a diagrammatic representation of a distributed networkcomputing environment where embodiments disclosed can be implemented. Inthe example of FIG. 7, network computing environment 700 may includenetwork 730 that can be bi-directionally coupled to user computer 712,ECM system 715, document capture server 714, and AI platform server 716.Document capture server 714 can be bi-directionally coupled to database738 and AI platform server 716 can be bi-directionally coupled to datastore 718. Network 730 may represent a combination of wired and wirelessnetworks that network computing environment 700 may utilize for varioustypes of network communications known to those skilled in the art.

For the purpose of illustration, a single system is shown for each ofuser computer 712, ECM system 715, document capture server 714, and AIplatform server 716. However, within each of user computer 712, ECMsystem 715, document capture server 714, and AI platform server 716, aplurality of computers (not shown) may be interconnected to each otherover network 730. For example, a plurality of user computers may becommunicatively connected over network 730 to document capture server714 operating in an enterprise computing environment and a plurality ofuser computers may be communicatively connected over network 730 to ECMsystem 715 and/or AI platform server 716.

User computer 712 may include data processing systems for communicatingwith document capture server 714. For example, user computer 712 caninclude central processing unit (“CPU”) 720, read-only memory (“ROM”)722, random access memory (“RAM”) 724, hard drive (“HD”) or storagememory 726, and input/output device(s) (“I/O”) 728. I/O 729 can includea keyboard, monitor, printer, electronic pointing device (e.g., mouse,trackball, stylus, etc.), or the like. User computer 712 can include adesktop computer, a laptop computer, a personal digital assistant, acellular phone, or nearly any device capable of communicating over anetwork.

Likewise, ECM system 715 may include data processing systems forcommunicating with document capture server 714 and/or AI platform server716. Document capture server 714 may include CPU 740, ROM 742, RAM 744,HD 746, and I/O 748 and AI platform server 716 may include CPU 760, ROM762, RAM 764, HD 766, and I/O 768. Document capture server 714 and AIplatform server 716 may each include one or more modules and UIsconfigured for providing services to user computer 712 and ECM system715 over network 730. ECM system 715 may be similar to AI platformserver 716 and can comprise CPU 750, ROM 752, RAM 754, HD 756, and I/O758. Many other alternative configurations are possible and known toskilled artisans.

Each of the computers in FIG. 7 may have more than one CPU, ROM, RAM,HD, I/O, or other hardware components. For the sake of brevity, eachcomputer is illustrated as having one of each of the hardwarecomponents, even if more than one is used. Each of computers 712, 714,715, and 716 is an example of a data processing system. ROM 722, 742,752, and 762; RAM 724, 744, 754, and 764; HD 726, 746, 756, and 766; andpermission data store 718 and content server database 738 can includemedia that can be read by CPU 720, 740, 750, or 760. Therefore, thesetypes of memories include non-transitory computer-readable storagemedia. These memories may be internal or external to computers 712, 714,715, or 716.

Portions of the methods described herein may be implemented in suitablesoftware code that may reside within ROM 722, 742, 752, or 762; RAM 724,744, 754, or 764; or HD 726, 746, 756, or 766. In addition to thosetypes of memories, the instructions in an embodiment disclosed hereinmay be contained on a data storage device with a differentcomputer-readable storage medium, such as a hard disk. Alternatively,the instructions may be stored as software code elements on a datastorage array, magnetic tape, floppy diskette, optical storage device,or other appropriate data processing system readable medium or storagedevice.

Those skilled in the relevant art will appreciate that the invention canbe implemented or practiced with other computer system configurations,including without limitation multi-processor systems, network devices,mini-computers, mainframe computers, data processors, and the like. Theinvention can be embodied in a computer, or a special purpose computeror data processor that is specifically programmed, configured, orconstructed to perform the functions described in detail herein. Theinvention can also be employed in distributed computing environments,where tasks or modules are performed by remote processing devices, whichare linked through a communications network such as a local area network(LAN), wide area network (WAN), and/or the Internet. In a distributedcomputing environment, program modules or subroutines may be located inboth local and remote memory storage devices. These program modules orsubroutines may, for example, be stored or distributed oncomputer-readable media, including magnetic and optically readable andremovable computer discs, stored as firmware in chips, as well asdistributed electronically over the Internet or over other networks(including wireless networks). Example chips may include ElectricallyErasable Programmable Read-Only Memory (EEPROM) chips. Embodimentsdiscussed herein can be implemented in suitable instructions that mayreside on a non-transitory computer readable medium, hardware circuitryor the like, or any combination and that may be translatable by one ormore server machines. Examples of a non-transitory computer readablemedium are provided below in this disclosure.

As is known to those skilled in the art, a suitable computer system caninclude a CPU, a ROM, a RAM, a HD, and I/O device(s). The I/O devicescan include a keyboard, monitor, printer, electronic pointing device(for example, mouse, trackball, stylus, touch pad, etc.), or the like.ROM, RAM, and HD are non-transitory computer memories for storingcomputer-executable instructions executable by the CPU or capable ofbeing compiled or interpreted to be executable by the CPU.

Suitable computer-executable instructions may reside on a non-transitorycomputer readable medium (e.g., ROM, RAM, and/or HD), hardware circuitryor the like, or any combination thereof. Within this disclosure, theterm “non-transitory computer readable medium” is not limited to ROM,RAM, and HD and can include any type of data storage medium that can beread by a processor. Examples of non-transitory computer-readablestorage media can include, but are not limited to, volatile andnon-volatile computer memories and storage devices such as random accessmemories, read-only memories, hard drives, data cartridges, directaccess storage device arrays, magnetic tapes, floppy diskettes, flashmemory drives, optical data storage devices, compact-disc read-onlymemories, and other appropriate computer memories and data storagedevices. Thus, a computer-readable medium may refer to a data cartridge,a data backup magnetic tape, a floppy diskette, a flash memory drive, anoptical data storage drive, a CD-ROM, ROM, RAM, HD, or the like.

The processes described herein may be implemented in suitablecomputer-executable instructions that may reside on a computer readablemedium (for example, a disk, CD-ROM, a memory, etc.). Alternatively, thecomputer-executable instructions may be stored as software codecomponents on a direct access storage device array, magnetic tape,floppy diskette, optical storage device, or other appropriatecomputer-readable medium or storage device.

Any suitable programming language can be used to implement the routines,methods or programs of embodiments of the invention described herein,including C, C++, Java, JavaScript, HTML, or any other programming orscripting code, etc. Other software/hardware/network architectures maybe used. For example, the functions of the disclosed embodiments may beimplemented on one computer or shared/distributed among two or morecomputers in or across a network. Communications between computersimplementing embodiments can be accomplished using any electronic,optical, radio frequency signals, or other suitable methods and tools ofcommunication in compliance with known network protocols.

Different programming techniques can be employed such as procedural orobject oriented. Any particular routine can execute on a single computerprocessing device or multiple computer processing devices, a singlecomputer processor or multiple computer processors. Data may be storedin a single storage medium or distributed through multiple storagemediums, and may reside in a single database or multiple databases (orother data storage techniques). Although the steps, operations, orcomputations may be presented in a specific order, this order may bechanged in different embodiments. In some embodiments, to the extentmultiple steps are shown as sequential in this specification, somecombination of such steps in alternative embodiments may be performed atthe same time. The sequence of operations described herein can beinterrupted, suspended, or otherwise controlled by another process, suchas an operating system, kernel, etc. The routines can operate in anoperating system environment or as stand-alone routines. Functions,routines, methods, steps, and operations described herein can beperformed in hardware, software, firmware or any combination thereof.

Embodiments described herein can be implemented in the form of controllogic in software or hardware or a combination of both. The controllogic may be stored in an information storage medium, such as acomputer-readable medium, as a plurality of instructions adapted todirect an information processing device to perform a set of stepsdisclosed in the various embodiments. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the invention.

It is also within the spirit and scope of the invention to implement insoftware programming or code an of the steps, operations, methods,routines or portions thereof described herein, where such softwareprogramming or code can be stored in a computer-readable medium and canbe operated on by a processor to permit a computer to perform any of thesteps, operations, methods, routines or portions thereof describedherein. The invention may be implemented by using software programmingor code in one or more digital computers, by using application specificintegrated circuits, programmable logic devices, field programmable gatearrays, optical, chemical, biological, quantum or nano-engineeredsystems, components, and mechanisms may be used. In general, thefunctions of the invention can be achieved by any means as is known inthe art. For example, distributed, or networked systems, components, andcircuits can be used. In another example, communication or transfer (orotherwise moving from one place to another) of data may be wired,wireless, or by any other means.

A “computer-readable medium” may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, system, ordevice. The computer readable medium can be, by way of example only butnot by limitation, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, system, device,propagation medium, or computer memory. Such computer-readable mediumshall generally be machine readable and include software programming orcode that can be human readable (e.g., source code) or machine readable(e.g., object code). Examples of non-transitory computer-readable mediacan include random access memories, read-only memories, hard drives,data cartridges, magnetic tapes, floppy diskettes, flash memory drives,optical data storage devices, compact-disc read-only memories, and otherappropriate computer memories and data storage devices. In anillustrative embodiment, some or all of the software components mayreside on a single server computer or on any combination of separateserver computers. As one skilled in the art can appreciate, a computerprogram product implementing an embodiment disclosed herein may compriseone or more non-transitory computer readable media storing computerinstructions translatable by one or more processors in a computingenvironment.

A “processor” includes any, hardware system, mechanism or component thatprocesses data, signals or other information. A processor can include asystem with a central processing unit, multiple processing units,dedicated circuitry for achieving functionality, or other systems.Processing need not be limited to a geographic location, or havetemporal limitations. For example, a processor can perform its functionsin “real-time,” “offline,” in a “batch mode,” etc. Portions ofprocessing can be performed at different times and at differentlocations, by different (or the same) processing systems.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having,” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, product, article, orapparatus.

Furthermore, the term “or” as used herein is generally intended to mean“and/or” unless otherwise indicated. For example, a condition A or B issatisfied by any one of the following: A is true (or present) and B isfalse (or not present), A is false (or not present) and B is true (orpresent), and both A and B are true (or present). As used herein,including the accompanying appendices, a term preceded by “a” or “an”(and “the” when antecedent basis is “a” or “an”) includes both singularand plural of such term, unless clearly indicated otherwise (i.e., thatthe reference “a” or “an” clearly indicates only the singular or onlythe plural). Also, as used in the description herein and in theaccompanying appendices, the meaning of “in” includes “in” and “on”unless the context clearly dictates otherwise.

Although the foregoing specification describes specific embodiments,numerous changes in the details of the embodiments disclosed herein andadditional embodiments will be apparent to, and may be made by, personsof ordinary skill in the art having reference to this disclosure. Inthis context, the specification and figures are to be regarded in anillustrative rather than a restrictive sense, and all such modificationsare intended to be included within the scope of this disclosure. Thescope of the present disclosure should be determined by the followingclaims and their legal equivalents.

What is claimed is:
 1. A method, comprising: receiving, by a documentcapture server computer from a document capture module running on aclient device, an image of a paper document; processing the image intoan electronic document, the processing producing textual content;extracting, by the document capture server computer, keywords from thetextual content; classifying the electronic document, the classifyingperformed by the document capture server computer based at least in parton the keywords extracted from the textual content; making, by thedocument capture server computer, a call to an artificial intelligence(AI) platform server computer, the call containing the textual contentand a class of the electronic document, wherein the AI platform servercomputer analyzes the textual content utilizing the class and returnsadditional data including a sentiment of the textual content;validating, by the document capture server computer, the additional datareturned by the AI platform server computer; and providing theelectronic document and the additional data to a content repository or acomputing facility operating in an enterprise computing environment. 2.The method according to claim 1, wherein the call includes anidentification of a knowledge base accessible by the AI platform servercomputer, the knowledge base specific to the class of the electronicdocument.
 3. The method according to claim 1, wherein the additionaldata includes at least an entity, a summary, a category, or a conceptthat the AI platform server computer learned from the textual content.4. The method according to claim 1, further comprising: providing avalidated concept or entity from the validating to the AI platformserver computer to improve future analyses by the AI platform servercomputer.
 5. The method according to claim 1, wherein the processingcomprises performing at least a format conversion on the image, an imageenhancement on the image, or an optical character recognition procedureon the image.
 6. The method according to claim 1, further comprising:determining a graphical layout of the electronic document; andclassifying the electronic document based at least in part on thegraphical layout of the electronic document.
 7. The method according toclaim 1, further comprising: performing a zonal extraction of a form inthe image; or performing a freeform extraction for a regular expressionin the image.
 8. A system, comprising: a processor; a non-transitorycomputer-readable medium; and stored instructions translatable by theprocessor to perform: receiving, from a document capture module runningon a client device, an image of a paper document; processing the imageinto an electronic document, the processing producing textual content;extracting keywords from the textual content; classifying the electronicdocument, the classifying based at least in part on the keywordsextracted from the textual content; making a call to an artificialintelligence (AI) platform server computer, the call containing thetextual content and a class of the electronic document, wherein the AIplatform server computer analyzes the textual content utilizing theclass and returns additional data including a sentiment of the textualcontent; validating the additional data returned by the AI platformserver computer; and providing the electronic document and theadditional data to a content repository or a computing facilityoperating in an enterprise computing environment.
 9. The system of claim8, wherein the call includes an identification of a knowledge baseaccessible by the AI platform server computer, the knowledge basespecific to the class of the electronic document.
 10. The system ofclaim 8, wherein the additional data includes at least an entity, asummary, a category, or a concept that the AI platform server computerlearned from the textual content.
 11. The system of claim 8, wherein thestored instructions are further translatable by the processor toperform: providing a validated concept or entity from the validating tothe AI platform server computer to improve future analyses by the AIplatform server computer.
 12. The system of claim 8, wherein theprocessing comprises performing at least a format conversion on theimage, an image enhancement on the image, or an optical characterrecognition procedure on the image.
 13. The system of claim 8, whereinthe stored instructions are further translatable by the processor toperform: determining a graphical layout of the electronic document; andclassifying the electronic document based at least in part on thegraphical layout of the electronic document.
 14. The system of claim 8,wherein the stored instructions are further translatable by theprocessor to perform: performing a zonal extraction of a form in theimage; or performing a freeform extraction for a regular expression inthe image.
 15. A computer program product comprising a non-transitorycomputer readable medium storing instructions translatable by aprocessor to perform: receiving, from a document capture module runningon a client device, an image of a paper document; processing the imageinto an electronic document, the processing producing textual content;extracting keywords from the textual content; classifying the electronicdocument, the classifying based at least in part on the keywordsextracted from the textual content; making a call to an artificialintelligence (AI) platform server computer, the call containing thetextual content and a class of the electronic document, wherein the AIplatform server computer analyzes the textual content utilizing theclass and returns additional data including a sentiment of the textualcontent; validating the additional data returned by the AI platformserver computer; and providing the electronic document and theadditional data to a content repository or a computing facilityoperating in an enterprise computing environment.
 16. The computerprogram product of claim 15, wherein the call includes an identificationof a knowledge base accessible by the AI platform server computer, theknowledge base specific to the class of the electronic document.
 17. Thecomputer program product of claim 15, wherein the additional dataincludes at least an entity, a summary, a category, or a concept thatthe AI platform server computer learned from the textual content. 18.The computer program product of claim 15, wherein the instructions arefurther translatable by the processor to perform: providing a validatedconcept or entity from the validating to the AI platform server computerto improve future analyses by the AI platform server computer.
 19. Thecomputer program product of claim 15, wherein the processing comprisesperforming at least a format conversion on the image, an imageenhancement on the image, or an optical character recognition procedureon the image.
 20. The computer program product of claim 15, wherein theinstructions are further translatable by the processor to perform:determining a graphical layout of the electronic document; andclassifying the electronic document based at least in part on thegraphical layout of the electronic document.